docs(vertex): add concise PayGo/Priority guide with cost-tracking flow

Document how to send Vertex Priority PayGo headers and explain how trafficType maps to service-tier pricing in LiteLLM, including an embedded flow diagram for quick understanding.

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Sameer Kankute
2026-03-18 17:49:03 +05:30
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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Vertex AI PayGo and Priority
## Priority PayGo
LiteLLM supports Priority PayGo.
Send a priority header, get priority queueing, and pay priority token rates.
:::info Which models support Priority PayGo?
As of this writing: `gemini/gemini-2.5-pro`, `vertex_ai/gemini-3-pro-preview`, `vertex_ai/gemini-3.1-pro-preview`, `vertex_ai/gemini-3-flash-preview`, and their variants.
Check `supports_service_tier: true` in LiteLLM's [model pricing JSON](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
:::
### Send a priority request
Use this header:
`X-Vertex-AI-LLM-Shared-Request-Type: priority`
<Tabs>
<TabItem value="litellm-sdk" label="LiteLLM SDK">
```python
import litellm
response = litellm.completion(
model="vertex_ai/gemini-3-pro-preview",
messages=[{"role": "user", "content": "Summarize the Gettysburg Address."}],
vertex_project="YOUR_PROJECT_ID",
vertex_location="us-central1",
extra_headers={"X-Vertex-AI-LLM-Shared-Request-Type": "priority"},
)
print(response.choices[0].message.content)
```
</TabItem>
<TabItem value="proxy-config" label="Proxy config">
```yaml title="config.yaml"
model_list:
- model_name: gemini-priority
litellm_params:
model: vertex_ai/gemini-3-pro-preview
vertex_project: "YOUR_PROJECT_ID"
vertex_location: "us-central1"
vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS
extra_headers:
X-Vertex-AI-LLM-Shared-Request-Type: priority
```
```bash
curl http://localhost:4000/v1/chat/completions \
-H "Authorization: Bearer sk-your-key" \
-H "Content-Type: application/json" \
-d '{"model": "gemini-priority", "messages": [{"role": "user", "content": "Hello"}]}'
```
</TabItem>
<TabItem value="pass-through" label="Pass-through mode">
Use `x-pass-` so LiteLLM forwards provider-specific headers.
```bash
MODEL_ID="gemini-3-pro-preview-0325"
PROJECT_ID="YOUR_PROJECT_ID"
curl -X POST \
"${LITELLM_PROXY_BASE_URL}/vertex_ai/v1/projects/${PROJECT_ID}/locations/global/publishers/google/models/${MODEL_ID}:generateContent" \
-H "Authorization: Bearer sk-your-litellm-key" \
-H "Content-Type: application/json" \
-H "x-pass-X-Vertex-AI-LLM-Shared-Request-Type: priority" \
-d '{"contents": [{"role": "user", "parts": [{"text": "Hello!"}]}]}'
```
</TabItem>
</Tabs>
### How cost tracking works
![Vertex AI Priority PayGo Cost Tracking Flow](/img/vertex_cost_tracking_flow.svg)
**`trafficType` → `service_tier` mapping**
| `usageMetadata.trafficType` | `service_tier` | Pricing keys used |
|---|---|---|
| `ON_DEMAND` | `None` | `input_cost_per_token` |
| `ON_DEMAND_PRIORITY` | `"priority"` | `input_cost_per_token_priority` |
| `FLEX` / `BATCH` | `"flex"` | `input_cost_per_token_flex` |
If a tier-specific key is missing, LiteLLM falls back to standard pricing keys.
---
## Standard PayGo vs Provisioned Throughput
This is a different header from priority routing:
| Header value | Behavior |
|---|---|
| `X-Vertex-AI-LLM-Request-Type: shared` | Force standard PayGo (bypass PT) |
| `X-Vertex-AI-LLM-Request-Type: dedicated` | Force Provisioned Throughput only (`429` if exhausted) |
### Native route example
```python
import litellm
response = litellm.completion(
model="vertex_ai/gemini-2.0-flash",
messages=[{"role": "user", "content": "Hello!"}],
vertex_project="YOUR_PROJECT_ID",
vertex_location="us-central1",
extra_headers={"X-Vertex-AI-LLM-Request-Type": "shared"},
)
```
### Pass-through example
```bash
MODEL_ID="gemini-2.0-flash-001"
PROJECT_ID="YOUR_PROJECT_ID"
curl -X POST \
"${LITELLM_PROXY_BASE_URL}/vertex_ai/v1/projects/${PROJECT_ID}/locations/us-central1/publishers/google/models/${MODEL_ID}:generateContent" \
-H "Authorization: Bearer sk-your-litellm-key" \
-H "Content-Type: application/json" \
-H "x-pass-X-Vertex-AI-LLM-Request-Type: shared" \
-d '{
"contents": [{"role": "user", "parts": [{"text": "Hello!"}]}]
}'
```
---
## Troubleshooting
**Q: What does `403 Permission denied` or `IAM_PERMISSION_DENIED` mean?**
A: The service account or Application Default Credentials (ADC) user does not have the `roles/aiplatform.user` role. To resolve this, re-run the `gcloud projects add-iam-policy-binding` command as shown above in the guide.
**Q: What should I do if I get a `429 Quota exceeded` error?**
A: This means you've hit the per-region QPM (queries per minute) or TPM (tokens per minute) quota. You can:
- Request a quota increase from the [GCP Quotas console](https://console.cloud.google.com/iam-admin/quotas)
- Add more regions to your LiteLLM configuration for load balancing (see the region balancing guide above)
- Upgrade to [Provisioned Throughput](https://cloud.google.com/vertex-ai/generative-ai/docs/provisioned-throughput) for guaranteed capacity
**Q: How do I fix the `VERTEXAI_PROJECT not set` error?**
A: Either pass the `vertex_project` parameter explicitly in your LiteLLM call, or set the `VERTEXAI_PROJECT` environment variable before running your code.
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